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A4M33DZO	Digital image			Extent of teaching:	2P+2C
Guarantors:		Roles:	PO,V	Language of teaching:	CS
Teachers:				Completion:	Z,ZK
Responsible Department:	13133	Credits:	6	Semester:	Z

Anotation:

First, the subject teaches how to process two-dimensional image as a signal without interpretation. Image acquisition, linear and nonlinear preprocessing methods and image compression will be studied. Second, image segmentation and registration methods will be taught. Studied topics will be practised on practical examples in order to obtain also practical skills.

Course outlines:

1.		Digital image processing vs. computer vision. Objects in images. Digital image. Distance transform. Brightness histogram.
2.		Physical foundation of images. Image acquisition from geometric and radiometric point of view.
3.		Processing in the spatial domain. Convolution. Correlation. Noise filtration. Linear and nonlinear methods.
4.		Fourier transform. Derivation of the sampling theorem. Frequency filtration of images. Image restauration.
5.		Brightness and geometric transformations, interpolation. Registration I.
6.		Edge detection. Multiscale image processing.
7.		Color images and processing of color images.
8.		Segmentation I.
9.		Segmentation II.
10.		Registration II.
11.		Image compression.
12.		Mathematical morphology.
13.		Reserve.

Exercises outline:

1.		MATLAB. Homework 1 assignment (image acquisition).
2.		Constultations. Solving the homework.
3.		Constultations. Solving the homework.
4.		Constultations. Solving the homework.
5.		Homework 1 handover. Homework 2 assignment (Fourier transformation).
6.		Constultations. Solving the homework.
7.		Constultations. Solving the homework.
8.		Constultations. Solving the homework.
9.		Homework 2 handover. Homework 3 assignment (image segmentation).
10.		Constultations. Solving the homework.
11.		Constultations. Solving the homework.
12.		Consultations. Homework 3 handover.
13.		Written test. Presentation of several best student homeworks.

Literature:

1.		Šonka, M., Hlaváč, V., Boyle, R.D.: Image processing, analysis and machine vision. 3. vydání, Thomson Learning, Toronto, Canada, 2007.
2.		Svoboda, T., Kybic, J., Hlaváč, V.: Image processing, analysis and

machine vision. The MATLAB companion, Thomson Learning, Toronto, Canada, 2007.

Requirements:

It is expected that the student is familiar with calculus, linear algebra, probability and statistics to the depth taught at FEL CVUT.

Note:

https://cw.felk.cvut.cz/doku.php/courses/a4m33dzo/start

Webpage:

http://cw.felk.cvut.cz/doku.php/courses/a4m33dzo/start

Keywords:

digital image processing, image acquisition, Fourier transformation

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
MPIB	Common courses	V	–
MPOI3	Computer Vision and Image Processing	PO	1
MPKME1	Wireless Communication	V	1
MPKME5	Systems of Communication	V	1
MPKME4	Networks of Electronic Communication	V	1
MPKME3	Electronics	V	1
MPKME2	Multimedia Technology	V	1
MPEEM1	Technological Systems	V	1
MPEEM5	Economy and Management of Electrical Engineering	V	1
MPEEM4	Economy and Management of Power Engineering	V	1
MPEEM3	Electrical Power Engineering	V	1
MPEEM2	Electrical Machines, Apparatus and Drives	V	1
MPKYR4	Aerospace Systems	V	1
MPKYR1	Robotics	V	1
MPKYR3	Systems and Control	V	1
MPKYR2	Sensors and Instrumentation	V	1

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Page updated 12.12.2019 17:52:04, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)